# Validate using IoU
if episode_num % 100 == 0:
policy_estimator.validate_iou(train_env, max_timesteps_per_episode=max_timesteps_per_episode,
summary_prefix='train')
policy_estimator.validate_iou(valid_env, max_timesteps_per_episode=max_timesteps_per_episode,
summary_prefix='valid')
max_timesteps = 10
training_env, validation_env = [PolygonEnv(d) for d in
get_train_and_valid_datasets('/home/wesley/data/polygons_dataset_2',
max_timesteps=10,
image_size=28,
prediction_size=28,
history_length=2,
is_local=True,
load_max_images=2,
validation_set_percentage=0.5)]
logdir = '/home/wesley/data/{}/'.format(os.path.splitext(os.path.basename(__file__))[0])
reset_dir(logdir)
with tf.Session() as sess:
tf.Variable(0, name="global_step", trainable=False)
summary_writer = tf.summary.FileWriter(logdir)
policy_estimator = ExperimentPolicyEstimator(image_size=28, action_size=28,
tf_session=sess, summary_writer=summary_writer,
max_timesteps=max_timesteps)
summary_writer.add_graph(sess.graph)
sess.run(tf.global_variables_initializer())
from collections import defaultdict
import numpy as np
from Dataset import get_train_and_valid_datasets
image_size = 28
prediction_size = 28
max_timesteps = 10
history_length = 2
print('Loading dataset from numpy archive...')
training_set, validation_set = get_train_and_valid_datasets(
'/home/wesley/docker_data/polygons_dataset_3',
max_timesteps=5,
image_size=image_size,
prediction_size=prediction_size,
history_length=history_length,
is_local=True,
validation_set_percentage=0)
print('Done!')
print('Training set')
batch_images = training_set._images
batch_verts = training_set._vertices
vertex_counts = defaultdict(lambda: 0)
max = 0
min = 1000
for idx, (image, vertices) in enumerate(zip(batch_images, batch_verts)):
l = len(vertices)
vertex_counts[l] += 1
return self._prediction_logits
if __name__ == '__main__':
import shutil
image_size = 28
prediction_size = 28
max_timesteps = 5
history_length = 1
global_step = tf.Variable(0, name='global_step', trainable=False)
train_data, valid_data = get_train_and_valid_datasets(
'/data/polygons_dataset_3',
max_timesteps=max_timesteps,
image_size=image_size,
prediction_size=prediction_size,
history_length=history_length,
is_local=True,
load_max_images=100000)
with tf.Session() as sess:
model_dir = '/data/{}/'.format(
os.path.splitext(os.path.basename(__file__))[0])
if os.path.exists(model_dir):
shutil.rmtree(model_dir)
model = ExperimentModel(sess, max_timesteps, image_size,
prediction_size, history_length, model_dir)
sess.run(tf.global_variables_initializer())
total_steps = 100000
# Wait until at least step 20000 to see decent? results
import matplotlib.pyplot as plt
from scipy.misc import imresize
from Dataset import get_train_and_valid_datasets
from util import *
image_size = 224
prediction_size = 28
max_timesteps = 5
history_length = 2
print('Loading dataset from numpy archive...')
training_set, validation_set = get_train_and_valid_datasets('/home/wesley/data/tiny-polygons',
max_timesteps=5,
image_size=image_size,
prediction_size=prediction_size,
history_length=history_length,
is_local=True)
print('Done!')
batch = training_set.get_batch_for_rnn(batch_size=len(training_set))
for i, (d, image, histories, targets, vertices) in enumerate(zip(*batch)):
print('Shape {} (duration={})...'.format(i, d), end='')
fig, ax = plt.subplots()
plt.imshow(imresize(image, prediction_size / image_size, interp='nearest'))
# Ground truth
for e, v in enumerate(vertices):
ax.add_artist(plt.Circle(v, 0.5, color='lightgreen'))
# plt.text(v[0], v[1], e, color='forestgreen')
for a, b in iterate_in_ntuples(vertices, n=2):
ax.add_line(matplotlib.lines.Line2D([a[0], b[0]], [a[1], b[1]], color='forestgreen'))
